如何通过Elasticsearch模糊匹配电子邮件或电话？

Question

我想通过Elasticsearch为电子邮件或电话进行模糊匹配.例如:

匹配所有电子邮件结束 @gmail.com

要么

匹配所有电话开头136.

我知道我可以使用通配符,

{
 "query": {
    "wildcard" : {
      "email": "*gmail.com"
    }
  }
}

但表现很差.我试着用regexp:

{"query": {"regexp": {"email": {"value": "*163\.com*"} } } }

但是不起作用.

是否有更好的方法来制作它？

curl -XGET localhost:9200/user_data

{
    "user_data": {
        "aliases": {},
        "mappings": {
            "user_data": {
                "properties": {
                    "address": {
                        "type": "string"
                    },
                    "age": {
                        "type": "long"
                    },
                    "comment": {
                        "type": "string"
                    },
                    "created_on": {
                        "type": "date",
                        "format": "dateOptionalTime"
                    },
                    "custom": {
                        "properties": {
                            "key": {
                                "type": "string"
                            },
                            "value": {
                                "type": "string"
                            }
                        }
                    },
                    "gender": {
                        "type": "string"
                    },
                    "name": {
                        "type": "string"
                    },
                    "qq": {
                        "type": "string"
                    },
                    "tel": {
                        "type": "string"
                    },
                    "updated_on": {
                        "type": "date",
                        "format": "dateOptionalTime"
                    },
                }
            }
        },
        "settings": {
            "index": {
                "creation_date": "1458832279465",
                "uuid": "Fbmthc3lR0ya51zCnWidYg",
                "number_of_replicas": "1",
                "number_of_shards": "5",
                "version": {
                    "created": "1070299"
                }
            }
        },
        "warmers": {}
    }
}

映射:

{
  "settings": {
    "analysis": {
      "analyzer": {
        "index_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "digit_edge_ngram_tokenizer",
          "filter": [ "trim" ]
        },
        "search_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "keyword",
          "filter": [ "trim" ]
        },
        "index_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "name_ngram_filter", "trim" ]
        },
        "search_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "trim" ]
        }
      },
      "char_filter": {
        "digit_only": {
          "type": "pattern_replace",
          "pattern": "\\D+",
          "replacement": ""
        }
      },
      "tokenizer": {
        "digit_edge_ngram_tokenizer": {
          "type": "edgeNGram",
          "min_gram": "3",
          "max_gram": "15",
          "token_chars": [ "digit" ]
        }
      },
      "filter": {
        "name_ngram_filter": {
          "type": "ngram",
          "min_gram": "3",
          "max_gram": "20"
        }
      }
    }
  },
  "mappings" : {
    "user_data" : {
      "properties" : {
        "name" : {
          "type" : "string",
          "analyzer" : "ik"
        },
        "age" : {
          "type" : "integer"
        },
        "gender": {
          "type" : "string"
        },
        "qq" : {
          "type" : "string"
        },
        "email" : {
          "type" : "string",
          "analyzer": "index_email_analyzer",
          "search_analyzer": "search_email_analyzer"
        },
        "tel" : {
          "type" : "string",
          "analyzer": "index_phone_analyzer",
          "search_analyzer": "search_phone_analyzer"
        },
        "address" : {
          "type": "string",
          "analyzer" : "ik"
        },
        "comment" : {
          "type" : "string",
          "analyzer" : "ik"
        },
        "created_on" : {
          "type" : "date",
          "format" : "dateOptionalTime"
        },
        "updated_on" : {
          "type" : "date",
          "format" : "dateOptionalTime"
        },
        "custom": {
          "type" : "nested",
          "properties" : {
            "key" : {
              "type" : "string"
            },
            "value" : {
              "type" : "string"
            }
          }
        }
      }
    }
  }
}

Answer 1

一种简单的方法是创建一个自定义分析器,它使用电子邮件的n-gram标记过滤器(=>见下面index_email_analyzer和search_email_analyzer+ email_url_analyzer用于精确的电子邮件匹配)和电话的edge-ngram标记过滤器(=>见下文index_phone_analyzer和search_phone_analyzer).

完整的索引定义如下.

PUT myindex
{
  "settings": {
    "analysis": {
      "analyzer": {
        "email_url_analyzer": {
          "type": "custom",
          "tokenizer": "uax_url_email",
          "filter": [ "trim" ]
        },
        "index_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "digit_edge_ngram_tokenizer",
          "filter": [ "trim" ]
        },
        "search_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "keyword",
          "filter": [ "trim" ]
        },
        "index_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "name_ngram_filter", "trim" ]
        },
        "search_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "trim" ]
        }
      },
      "char_filter": {
        "digit_only": {
          "type": "pattern_replace",
          "pattern": "\\D+",
          "replacement": ""
        }
      },
      "tokenizer": {
        "digit_edge_ngram_tokenizer": {
          "type": "edgeNGram",
          "min_gram": "1",
          "max_gram": "15",
          "token_chars": [ "digit" ]
        }
      },
      "filter": {
        "name_ngram_filter": {
          "type": "ngram",
          "min_gram": "1",
          "max_gram": "20"
        }
      }
    }
  },
  "mappings": {
    "your_type": {
      "properties": {
        "email": {
          "type": "string",
          "analyzer": "index_email_analyzer",
          "search_analyzer": "search_email_analyzer"
        },
        "phone": {
          "type": "string",
          "analyzer": "index_phone_analyzer",
          "search_analyzer": "search_phone_analyzer"
        }
      }
    }
  }
}

现在,让我们一点一点地剖析它.

对于该phone领域,想法是使用index_phone_analyzer,使用edge-ngram标记器索引电话值,以索引电话号码的所有前缀.所以,如果您的电话号码1362435647,下面的标记会产生:1,13,136,1362,13624,136243,1362435,13624356,13624356,136243564,1362435647.

然后在搜索时我们使用另一个分析器search_phone_analyzer,它只需输入输入数字(例如136)并phone使用简单match或term查询将其与字段匹配:

POST myindex
{ 
    "query": {
        "term": 
            { "phone": "136" }
    }
}

对于该email字段,我们以类似的方式进行,因为我们index_email_analyzer使用一个ngram令牌过滤器索引电子邮件值,该过滤器将生成所有可能的长度(1到20个字符之间)的令牌,这些令牌可以从电子邮件价值 例如:john@gmail.com将切分给j,jo,joh,... gmail.com,... john@gmail.com.

然后在搜索时,我们将使用另一个称为search_email_analyzer输入的分析器,并尝试将其与索引标记匹配.

POST myindex
{ 
    "query": {
        "term": 
            { "email": "@gmail.com" }
    }
}

该email_url_analyzer分析仪并没有在这个例子中使用,但我万一你需要匹配的确切电子邮件值包含它.